Datasaur
Visit our websitePricingBlogPlaygroundAPI Docs
  • Welcome to Datasaur
    • Getting started with Datasaur
  • Data Studio Projects
    • Labeling Task Types
      • Span Based
        • OCR Labeling
        • Audio Project
      • Row Based
      • Document Based
      • Bounding Box
      • Conversational
      • Mixed Labeling
      • Project Templates
        • Test Project
    • Creating a Project
      • Data Formats
      • Data Samples
      • Split Files
      • Consensus
      • Dynamic Review Capabilities
    • Pre-Labeled Project
    • Let's Get Labeling!
      • Span Based
        • Span + Line Labeling
      • Row & Document Based
      • Bounding Box Labeling
      • Conversational Labeling
      • Label Sets / Question Sets
        • Dynamic Question Set
      • Multiple Label Sets
    • Reviewing Projects
      • Review Sampling
    • Adding Documents to an Ongoing Project
    • Export Project
  • LLM Projects
    • LLM Labs Introduction
    • Sandbox
      • Direct Access LLMs
      • File Attachment
      • Conversational Prompt
    • Deployment
      • Deployment API
    • Knowledge base
      • External Object Storage
      • File Properties
    • Models
      • Amazon SageMaker JumpStart
      • Amazon Bedrock
      • Open AI
      • Azure OpenAI
      • Vertex AI
      • Custom model
      • Fine-tuning
      • LLM Comparison Table
    • Evaluation
      • Automated Evaluation
        • Multi-application evaluation
        • Custom metrics
      • Ranking (RLHF)
      • Rating
      • Performance Monitoring
    • Dataset
    • Pricing Plan
  • Workspace Management
    • Workspace
    • Role & Permission
    • Analytics
      • Inter-Annotator Agreement (IAA)
        • Cohen's Kappa Calculation
        • Krippendorff's Alpha Calculation
      • Custom Report Builder
      • Project Report
      • Evaluation Metrics
    • Activity
    • File Transformer
      • Import Transformer
      • Export Transformer
      • Upload File Transformer
      • Running File Transformer
    • Label Management
      • Label Set Management
      • Question Set Management
    • Project Management
      • Self-Assignment
        • Self-Unassign
      • Transfer Assignment Ownership
      • Reset Labeling Work
      • Mark Document as Complete
      • Project Status Workflow
        • Read-only Mode
      • Comment Feature
      • Archive Project
    • Automation
      • Action: Create Projects
  • Assisted Labeling
    • ML Assisted Labeling
      • Amazon Comprehend
      • Amazon SageMaker
      • Azure ML
      • CoreNLP NER
      • CoreNLP POS
      • Custom API
      • FewNERD
      • Google Vertex AI
      • Hugging Face
      • LLM Assisted Labeling
        • Prompt Examples
        • Custom Provider
      • LLM Labs (beta)
      • NLTK
      • Sentiment Analysis
      • spaCy
      • SparkNLP NER
      • SparkNLP POS
    • Data Programming
      • Example of Labeling Functions
      • Labeling Function Analysis
      • Inter-Annotator Agreement for Data Programming
    • Predictive Labeling
  • Assisted Review
    • Label Error Detection
  • Building Your Own Model
    • Datasaur Dinamic
      • Datasaur Dinamic with Hugging Face
      • Datasaur Dinamic with Amazon SageMaker Autopilot
  • Advanced
    • Script-Generated Question
    • Shortcuts
    • Extensions
      • Labels
      • Review
      • Document and Row Labeling
      • Bounding Box Labels
      • List of Files
      • Comments
      • Analytics
      • Dictionary
      • Search
      • Labeling Guidelines
      • Metadata
      • Grammar Checker
      • ML Assisted Labeling
      • Data Programming
      • Datasaur Dinamic
      • Predictive Labeling
      • Label Error Detection
      • LLM Sandbox
    • Tokenizers
  • Integrations
    • External Object Storage
      • AWS S3
        • With IRSA
      • Google Cloud Storage
      • Azure Blob Storage
    • SAML
      • Okta
      • Microsoft Entra ID
    • SCIM
      • Okta
      • Microsoft Entra ID
    • Webhook Notifications
      • Webhook Signature
      • Events
      • Custom Headers
    • Robosaur
      • Commands
        • Create Projects
        • Apply Project Tags
        • Export Projects
        • Generate Time Per Task Report
        • Split Document
      • Storage Options
  • API
    • Datasaur APIs
    • Credentials
    • Create Project
      • New mutation (createProject)
      • Python Script Example
    • Adding Documents
    • Labeling
      • Create Label Set
      • Add Label Sets into Existing Project
      • Get List of Label Sets in a Project
      • Add Label Set Item into Project's Label Set
      • Programmatic API Labeling
      • Inserting Span and Arrow Label into Document
    • Export Project
      • Custom Webhook
    • Get Data
      • Get List of Projects
      • Get Document Information
      • Get List of Tags
      • Get Cabinet
      • Export Team Overview
      • Check Job
    • Custom OCR
      • Importable Format
    • Custom ASR
    • Run ML-Assisted Labeling
  • Security and Compliance
    • Security and Compliance
      • 2FA
  • Compatibility & Updates
    • Common Terminology
    • Recommended Machine Specifications
    • Supported Formats
    • Supported Languages
    • Release Notes
      • Version 6
        • 6.111.0
        • 6.110.0
        • 6.109.0
        • 6.108.0
        • 6.107.0
        • 6.106.0
        • 6.105.0
        • 6.104.0
        • 6.103.0
        • 6.102.0
        • 6.101.0
        • 6.100.0
        • 6.99.0
        • 6.98.0
        • 6.97.0
        • 6.96.0
        • 6.95.0
        • 6.94.0
        • 6.93.0
        • 6.92.0
        • 6.91.0
        • 6.90.0
        • 6.89.0
        • 6.88.0
        • 6.87.0
        • 6.86.0
        • 6.85.0
        • 6.84.0
        • 6.83.0
        • 6.82.0
        • 6.81.0
        • 6.80.0
        • 6.79.0
        • 6.78.0
        • 6.77.0
        • 6.76.0
        • 6.75.0
        • 6.74.0
        • 6.73.0
        • 6.72.0
        • 6.71.0
        • 6.70.0
        • 6.69.0
        • 6.68.0
        • 6.67.0
        • 6.66.0
        • 6.65.0
        • 6.64.0
        • 6.63.0
        • 6.62.0
        • 6.61.0
        • 6.60.0
        • 6.59.0
        • 6.58.0
        • 6.57.0
        • 6.56.0
        • 6.55.0
        • 6.54.0
        • 6.53.0
        • 6.52.0
        • 6.51.0
        • 6.50.0
        • 6.49.0
        • 6.48.0
        • 6.47.0
        • 6.46.0
        • 6.45.0
        • 6.44.0
        • 6.43.0
        • 6.42.0
        • 6.41.0
        • 6.40.0
        • 6.39.0
        • 6.38.0
        • 6.37.0
        • 6.36.0
        • 6.35.0
        • 6.34.0
        • 6.33.0
        • 6.32.0
        • 6.31.0
        • 6.30.0
        • 6.29.0
        • 6.28.0
        • 6.27.0
        • 6.26.0
        • 6.25.0
        • 6.24.0
        • 6.23.0
        • 6.22.0
        • 6.21.0
        • 6.20.0
        • 6.19.0
        • 6.18.0
        • 6.17.0
        • 6.16.0
        • 6.15.0
        • 6.14.0
        • 6.13.0
        • 6.12.0
        • 6.11.0
        • 6.10.0
        • 6.9.0
        • 6.8.0
        • 6.7.0
        • 6.6.0
        • 6.5.0
        • 6.4.0
        • 6.3.0
        • 6.2.0
        • 6.1.0
        • 6.0.0
      • Version 5
        • 5.63.0
        • 5.62.0
        • 5.61.0
        • 5.60.0
  • Deployment
    • Self-Hosted
      • AWS Marketplace
        • Data Studio
        • LLM Labs
Powered by GitBook
On this page
  • Labeling Function Analysis Window
  • How to Improve Labeling Function Performance
  1. Assisted Labeling
  2. Data Programming

Labeling Function Analysis

Allows users to view the results of their labeling functions, including coverage, overlaps, and conflicts, and to improve performance by training the label model

Last updated 1 month ago

To get the results from labeling function analysis, you need to have some labeling functions and predict labels.

Labeling Function Analysis Window

The labeling function analysis window has two ways to view the results: by clicking on the labeling function button, or through the "See labeling function analysis" button after predicting the labels.

If you haven't predicted the labels, the labeling function analysis page will show an empty state.

After clicking predict labels, the results will be shown in the labeling function analysis. There are three metrics: coverage, overlaps, and conflicts.

  1. Coverage is the fraction of the dataset each labeling function labels.

  2. Overlaps are the fraction of the dataset where each labeling function and at least another labeling function label.

  3. Conflicts are the fraction of the dataset where each labeling function and at least another labeling function label, and they disagree.

If you have a new labeling function or have made changes to your labeling function, you need to re-predict labels in order to update the analysis value of the labeling function.

How to Improve Labeling Function Performance

The ideal situation for labeling function is to have high coverage, high overlap, and low conflicts. Below is a use case of labeling function performance conditions:

Fairly high coverage, high overlaps, and high conflicts.

It means our LFs can label a lot of data points and the majority of data points were assigned more than one LFs with different labels. We have one example of performance metrics value below.

  1. Coverage = 50%

  2. Overlaps = 30%

  3. Conflicts = 27%

This number shows that even though there is large coverage and overlaps, the disagreement between labeling functions happens in almost half of the coverage. To improve this, we need to train the label model to get the performance value between labeling functions. The performance value of labeling functions could estimate accuracies and correlations between labeling functions since we know some labeling functions could give high or low signals regarding the label.

Low coverage, high overlaps, and high conflicts.

We need to add several new labeling functions and try to identify which labeling function creates more conflicts by experimenting one by one. After identifying it, we can re-evaluate the labeling functions.

Data programming extension
Labeling function Analysis Empty State
Labeling function Analysis Result
Outdated labeling function value